Hyperspectral remote sensing of forage stoichiometric ratios in the senescent stage of alpine grasslands

文献类型： 外文期刊

第一作者： Gao, Jinlong

作者： Gao, Jinlong;Liang, Tiangang;Liu, Jie;Feng, Qisheng;Wu, Caixia;Zhang, Xuanfan;Gao, Jinlong;Liang, Tiangang;Liu, Jie;Feng, Qisheng;Wu, Caixia;Zhang, Xuanfan;Gao, Jinlong;Liang, Tiangang;Liu, Jie;Feng, Qisheng;Wu, Caixia;Zhang, Xuanfan;Gao, Jinlong;Liang, Tiangang;Liu, Jie;Feng, Qisheng;Wu, Caixia;Zhang, Xuanfan;Zhang, Dongmei;Wang, Zhiwei;Liang, Tiangang

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关键词： Grass senescence; Hyperspectral data; Stoichiometric ratio; Estimation model; Variable selection

期刊名称：FIELD CROPS RESEARCH （ 影响因子：5.6； 五年影响因子：6.1 ）

ISSN： 0378-4290

年卷期： 2024 年 313 卷

页码：

收录情况： SCI

摘要： Senescence is an important phenological stage in the life cycle of grassland plants, and estimating grass senescence in alpine grasslands is particularly crucial for animal husbandry and grazing the utilization of grassland resources for grazing. However, the application of proximal sensing technologies to capture the variations in forage growth parameters in the senescent stage, especially regarding some key stoichiometric ratios, still faces challenges associated with indiscernible spectral properties and indeterminate absorption and reflectance features. The present study aims to demonstrate the potential and effectiveness of applying hyperspectral data to assess the forage carbon -nitrogen (C:N) ratio and calcium -phosphorus (Ca:P) ratio during the senescent stage (September to November). A progressive variable dimensionality reduction strategy based on a genetic algorithm (GA) and hybrid optimization method (ant optimization colony (ACO) and extreme learning machine (ELM)) coupled with multiple linear and nonlinear statistical methods is applied to develop C:N ratio and Ca:P ratio estimation models via combination with data from 205 sample sites collected from six field campaigns (2016 -2019) on the eastern Tibetan Plateau. The results show that the important spectral bands favorable for forage C:N ratio and Ca:P ratio retrieval are generally in the red and shortwave infrared (SWIR) regions, and the proposed model presents satisfactory performance in the estimation of the forage C:N ratio (V -R 2 = 0.81, V-RMSE = 6.44) and Ca:P ratio (V -R 2 = 0.64, V-RMSE = 2.54) during senescence. Moreover, the model can effectively overcome the spatial differences among sampling areas and perform optimally in capturing the variations in the forage C:N ratio and Ca:P ratio in the early and middle stages of senescence. Overall, our study demonstrates that it is feasible and promising to estimate these stoichiometric ratios using hyperspectral feature bands during grass senescence at the canopy level, with potential applications that may further enhance the monitoring of forage quality and quantity.

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